Qualitative planning with quantitative constraints for online learning of robotic behaviours

This paper resolves previous problems in the Multi-Strategy architecture for online learning of robotic behaviours. The hybrid method includes a symbolic qualitative planner that constructs an approximate solution to a control problem. The approximate solution provides constraints for a numerical op- timisation algorithm, which is used to refine the qualitative plan into an operational policy. Introducing quantitative con- straints into the planner gives previously unachievable do- main independent reasoning. The method is demonstrated on a multi-tracked robot intended for urban search and rescue